Portable device positioning data processing method and apparatus, device, and storage medium

ABSTRACT

A method for processing positioning data of a mobile device is provided, comprising: acquiring a first original point set and a target point set by measuring an object surface with the mobile device; extracting feature points from the first original point set to obtain an original key point set; extracting feature points from the target point set to obtain a target key point set; performing a first registration operation on the original key point set and the target key point set to obtain a first model transformation parameter; transforming the first original point set by the first model transformation parameter to obtain a second original point set; performing a second registration operation on the second original point set and the target point set to obtain a second model transformation parameter; and acquiring third model transformation parameter based on the first model transformation parameter and the second model transformation parameter.

The present disclosure is a 35 U.S.C. 371 national phase application ofPCT International Application No. PCT/CN2020/132929, which is based onthe Chinese patent application with the application number of201911269684.4, the application date of Dec. 11, 2019, and the title of“Method, Device, Equipment and Storage Medium for Processing PositioningData of Mobile Device”, and claims the benefit and priority of theChinese patent application, the entire contents of which PCTInternational Application and which Chinese patent application isincorporated by reference into the present disclosure.

TECHNICAL FIELD

The present disclosure relates to the field of computer visiontechnology, and in particular, to a method, apparatus, device, andreadable storage medium for processing positioning data of a mobiledevice.

BACKGROUND

Computer vision is a science that studies how to make machines “see”.Further, it refers to the use of cameras and computers instead of humaneyes to identify, track, and measure targets, and further to performgraphics processing so as to make computer processing more suitable forhuman observation or transmission to instruments for detection. Applyingcomputer vision technology to indoor robot equipment plays an importantrole in solving problems such as indoor map construction, self-poseestimation, navigation and obstacle avoidance.

The realization of computer vision usually uses physical digitizationtechnology to collect data through measurement methods such as lidar.Restricted by the measurement equipment and environment, the acquisitionof complete measurement data on an object surface often requiresmultiple measurements. Since generally the point cloud data obtained byeach measurement only covers a part of the object surface, and there maybe translational and rotational dislocations, in order to obtain thepoint cloud data of the complete object surface, it is necessary tointegrate and register these local point cloud data. The point cloudregistration algorithm in the related art usually has problems such asslow speed, low accuracy and poor robustness, which may lead to poorreal-time performance, low accuracy and poor adaptability whenpositioning and navigating mobile devices such as indoor robots.

As mentioned above, how to provide a method for processing positioningdata with good real-time performance, high precision and strongadaptability has become an urgent problem to be solved.

The above information disclosed in this Background section is only forenhancement of understanding of the background of the present disclosureand therefore it may contain information that does not form the priorart that is already known to a person of ordinary skill in the art.

SUMMARY

The purpose of the present disclosure is to provide a method, device,apparatus and readable storage medium for processing positioning data ofa mobile device, to overcome at least to a certain extent disadvantagessuch as slow speed, low precision and poor robustness of the point cloudregistration algorithm in the related art, which may otherwise lead toproblems such as poor real-time performance, low accuracy and pooradaptability of mobile devices such as indoor robots when positioningand navigating.

Other features and advantages of the present disclosure will becomeapparent from the following detailed description, or be learned in partby practice of the present disclosure.

According to an aspect of the present disclosure, a method forprocessing positioning data of a mobile device is provided, including:acquiring a first original point set and a target point set by measuringan object surface with the mobile device; extracting feature points fromthe first original point set to obtain an original key point set;extracting feature points from the target point set to obtain a targetkey point set; performing a first registration operation on the originalkey point set and the target key point set to obtain a first modeltransformation parameter; transforming the first original point set bythe first model transformation parameter to obtain a second originalpoint set; performing a second registration operation on the secondoriginal point set and the target point set to obtain a second modeltransformation parameter; and acquiring a third model transformationparameter based on the first model transformation parameter and thesecond model transformation parameter, where the third modeltransformation parameter is configured to enable the mobile device toobtain data of the object surface for positioning by registration on thefirst original point set and the target point set.

According to an embodiment of the present disclosure, the performing thefirst registration operation on the original key point set and thetarget key point set to obtain the first model transformation parameterincludes: acquiring a closest point pair in the original key point setand the target key point set, where the closest point pair includes oneoriginal key point in the original key point set and one target keypoint in the target key point set, and a distance between the oneoriginal key point and the one target key point is less than or equal toa first preset threshold; and acquiring the first model transformationparameter based on the closest point pair.

According to an embodiment of the present disclosure, the acquiring theclosest point pair in the original key point set and the target keypoint set includes: acquiring a plurality of closest points in theoriginal key point set and the target key point set; and the acquiringthe first model transformation parameter based on the closest point pairincludes: acquiring a plurality of candidate model transformationparameters corresponding to the plurality of closest point pairs; foreach closest point pair in the plurality of closest point pairs,calculating a transformation error, with respect to the one target keypoint in the closest point pair, of each of other original key points inthe original key point set, except for the one original key point in theclosest point pair, after transformation based on the candidate modeltransformation parameter corresponding to the closest point pair;acquiring the number of the other original key points whosetransformation error corresponding to each closest point pair is lessthan or equal to a second preset threshold; selecting, as the firstmodel transformation parameter, the candidate model transformationparameter corresponding to the closest point pair with the largestnumber of the other original key points whose transformation error isless than or equal to the second preset threshold.

According to an embodiment of the present disclosure, the extracting thefeature points from the first original point set to obtain the originalkey point set, and the extracting the feature points from the targetpoint set to obtain the target key point set, include: dividing a spaceformed by the first original point set into a plurality of voxel gridswith a first preset side length; calculating the center of gravity of anoriginal point contained in each voxel grid of the plurality of voxelgrids with the first preset side length, where a set of the centers ofgravity of each voxel grid is the original key point set; dividing aspace formed by the first target point set into a plurality of voxelgrids with a second preset side length; and calculating the center ofgravity of a target point included in each voxel grid of the pluralityof voxel grids with the second preset side length, where a set of thecenters of gravity of each voxel grid is the target key point set.

According to an embodiment of the present disclosure, the performing thesecond registration operation on the second original point set and thetarget point set to obtain the second model transformation parameterincludes: calculating a variance on each dimension of each target pointin the target point set; selecting the dimension with the largestvariance as the registration dimension; constructing thehigh-dimensional index binary tree in the registration dimension of thetarget point set; searching for a closest point in the target point setof each second original point in the second original point set by usingthe high-dimensional index binary tree; calculating the distance betweeneach second original point and the respective closest pointrespectively; and selecting, as the second model transformationparameter, a model transformation parameter, with respect to arespective closest point, of a second original point in the secondoriginal point set with the smallest distance to the respective closestpoint.

According to an embodiment of the present disclosure, the searching forthe closest point in the target point set of each second original pointin the second original point set by using the high-dimensional indexbinary tree includes: querying the high-dimensional index binary treedownward according to a comparison result between each second originalpoint and each node of the high-dimensional index binary tree bystarting from a root node of the high-dimensional index binary tree foreach second original point, until a leaf node is reached; determiningwhether a distance between a node on a unqueried branch for each secondoriginal point on the high-dimensional index binary tree and the secondoriginal point is not less than the leaf node; and if the distancebetween the node on the unqueried branch and each second original pointis smaller than the leaf node, it is determined that the node on theunqueried branch for the second original point is the closest point.

According to an embodiment of the present disclosure, the determiningthe distance between the node on the unqueried branch for each secondoriginal point on the high-dimensional index binary tree and the secondoriginal point is not less than the leaf node, includes: sorting eachnode on the unqueried branch according to closeness of a value of theregistration dimension with respect to each second original point toobtain a priority node sequence; and based on the priority nodesequence, sequentially querying each node on the unqueried branch foreach second original point on the high-dimensional index binary tree, todetermine whether a distance between each node on the unqueried branchand the second original point is not less than the leaf node.

According to another aspect of the present disclosure, a device forprocessing positioning data of a mobile device is provided, including: adata acquisition module, configured to acquire a first original pointset and a target point set by measuring an object surface with themobile device; a feature extraction module, configured to extractfeature points from the first original point set to obtain an originalkey point set, and further configured to extract feature points from thetarget point set to obtain a target key point set; a first registrationmodule, configured to perform a first registration operation on theoriginal key point set and the target key point set to obtain a firstmodel transformation parameter, and further configured to transform thefirst original point set by the first model transformation parameter toobtain a second original point set; a second registration module,configured to perform a second registration operation on the secondoriginal point set and the target point set to obtain a second modeltransformation parameter; and a model acquisition module, configured toacquire a third model transformation parameter based on the first modeltransformation parameter and the second model transformation parameter,where the third model transformation parameter is configured to enablethe mobile device to obtain data of the object surface for positioningby registration on the first original point set and the target pointset.

According to yet another aspect of the present disclosure, there isprovided an apparatus, including: a memory, a processor, and executableinstructions stored in the memory and running in the processor, wherethe processor is configured to execute the executable instructions toimplement the method according to any one of the above embodiments.

According to yet another aspect of the present disclosure, there isprovided a computer-readable storage medium on which computer-executableinstructions are stored, where the computer-executable instructions areconfigured, when executed by a processor, to implement the methodaccording to any one of the above embodiments.

According to the method for processing positioning data of a mobiledevice provided by an embodiment of the present disclosure, the firstoriginal point set and the target point set are obtained by measuringthe object surface with the mobile device, feature points are extractedfrom the first original point set and the target point set respectivelyto obtain the original key point set and the target key point set, thefirst registration operation is performed on the original key point setand the target key point set to obtain the first model transformationparameter, and the first original point set is transformed by the firstmodel transformation parameter to obtain the second original point set.After that, the second registration operation is performed on the secondoriginal point set and the target point set to obtain the second modeltransformation parameter, and the third model transformation parameteris acquired based on the first model transformation parameter and thesecond model transformation parameter for registration on the firstoriginal point set and the target point set, which enables the mobiledevice to obtain data of the object surface for positioning, therebyimproving the processing accuracy of the positioning data of the mobiledevice to a certain extent.

It is to be understood that the foregoing general description and thefollowing detailed description are exemplary only and do not limit thepresent disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, features and advantages of the presentdisclosure will become more apparent from the detailed description ofexample embodiments thereof with reference to the accompanying drawings.

FIG. 1 shows a schematic structural diagram of a system for processingpositioning data of a mobile device in an embodiment of the presentdisclosure.

FIG. 2 shows a flowchart of a method for processing positioning data ofa mobile device in an embodiment of the present disclosure.

FIG. 3 shows a flowchart of a method for extracting feature points ofpositioning data of a mobile device in an embodiment of the presentdisclosure.

FIG. 4 shows a flowchart of another method for processing positioningdata of a mobile device in an embodiment of the present disclosure.

FIG. 5 shows a block diagram of an apparatus for processing positioningdata of a mobile device in an embodiment of the present disclosure.

FIG. 6 shows a schematic structural diagram of an electronic device inan embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Example embodiments will now be described more fully with reference tothe accompanying drawings. Example embodiments, however, may be embodiedin various forms and should not be construed as limited to the examplesset forth herein. Rather, these embodiments are provided so that thepresent disclosure will be thorough and complete, and will fully conveythe concept of example embodiments to those skilled in the art. Thedrawings are merely schematic illustrations of the present disclosureand are not necessarily drawn to scale. The same reference numerals inthe drawings denote the same or similar parts, and thus their repeateddescriptions will be omitted.

Furthermore, the described features, structures, or characteristics maybe combined in any suitable way in one or more embodiments. In thefollowing description, numerous specific details are provided in orderto give a thorough understanding of embodiments of the presentdisclosure. However, those skilled in the art will appreciate that thetechnical solutions of the present disclosure may be practiced withoutone or more of the specific details, or other methods, devices, steps,etc. may be employed. In other instances, well-known structures,methods, devices, implementations, or operations have not been shown ordescribed in detail to avoid obscuring aspects of the presentdisclosure.

In addition, the terms “first”, “second”, etc. are used for descriptivepurposes only, and should not be construed as indicating or implyingrelative importance or implying the number of indicated technicalfeatures. Thus, a feature with “first” or “second” ahead may expresslyor implicitly include one or more of that feature. In the description ofthe present disclosure, “plurality” means at least two, such as two,three, etc., unless expressly and specifically defined otherwise. Thesymbol “/” generally indicates that the related objects are an “or”relationship.

In the present disclosure, unless otherwise expressly specified andlimited, terms such as “connection” should be interpreted in a broadsense. For example, it may be an electrical connection or may be amutual communication. Alternatively, it may be a direct connection or anindirect connection through an intermediate medium. For those ofordinary skill in the art, the specific meanings of the above terms inthe present disclosure may be understood according to specificsituations.

As mentioned above, the point cloud registration algorithm in therelated art usually has problems such as slow speed, low accuracy andpoor robustness, which may lead to poor real-time performance, lowaccuracy and poor adaptability of mobile devices such as indoor robotswhen positioning and navigating. Therefore, the present disclosureprovides a method for processing positioning data of a mobile device.The first original point set and the target point set are acquired bymeasuring the object surface with the mobile device, and feature pointsare extracted from the first original point set and the target point setrespectively to obtain the original key point set and the target keypoint set. Further, the first registration operation is performed on theoriginal key point set and the target key point set to obtain the firstmodel transformation parameter, and the first original point set istransformed based on the first model transformation parameter to obtainthe second original point set. Then, the second registration operationis performed on the second original point set and the target point setto obtain the second model transformation parameter, and the third modeltransformation parameter is obtained based on the first modeltransformation parameter and the second model transformation parameterfor registration on the first original point set and the target pointset, which enables the mobile device to obtain data of the objectsurface for positioning, thereby improving the processing accuracy ofthe positioning data of the mobile device to a certain extent.

FIG. 1 shows an exemplary system architecture 100 to which the methodfor processing positioning data of a mobile device or the device forprocessing positioning data of a mobile device of the present disclosuremay be applied.

As shown in FIG. 1 , the system architecture 100 may include terminaldevices 101, 102, and 103, a network 104 and a server 105. The network104 is a medium used to provide a communication link between theterminal devices 101, 102, 103 and the server 105. The network 104 mayinclude various connection types, such as wired, wireless communicationlinks, or fiber optic cables, among others.

A user may use the terminal devices 101, 102, 103 to interact with theserver 105 through the network 104 to receive or send messages and thelike. Various communication client applications may be installed on theterminal devices 101, 102 and 103, such as a photographing application,an image recognition application, and the like.

The terminal devices 101, 102, 103 may be various electronic deviceshaving a display screen and supporting web browsing, including but notlimited to smart phones, tablet computers, laptop computers, desktopcomputers, and the like.

The server 105 may be a server that provides various services, such as abackground management server (just an example) that provides support forimage search websites browsed by the user using the terminal devices101, 102, and 103. The background management server may analyze andprocess the received initial images and other data, and feed back theimage fusion result to the terminal devices.

It should be understood that the numbers of terminal devices, networksand servers in FIG. 1 are merely illustrative. There may be any numberof terminal devices, networks and servers according to implementationneeds.

FIG. 2 is a flow chart of a method for processing positioning data of amobile device according to an exemplary embodiment. The method shown inFIG. 2 may be applied to, for example, the server side of the system forprocessing positioning data of the mobile device, and may bealternatively applied to the terminal device of the system forprocessing positioning data of the mobile device.

Referring to FIG. 2 , the method 20 provided by an embodiment of thepresent disclosure may include the following steps S202, S204, S206,S208, S210, S212, and S214.

In step S202, a first original point set and a target point set isacquired by measuring the object surface with the mobile device. Mobiledevices include intelligent robots, mobile photographing devices, andthe like. The mobile device may use lidar to measure indoor objects toobtain data of the object surface. The first original point set and thetarget point set are point cloud sets in different coordinate systemsobtained by the mobile device measuring the object surface, which may berotated and translated for registration into the same coordinate systemto obtain a more complete point cloud data of the object surface.

In some embodiments, the two point sets that need to be registered maybe far apart, and a translation operation may be performed beforeregistration, so as to reduce the calculation amount in searching forthe closest point. The translation operation may be performed in thefollowing ways.

Firstly, the following formulas are used to calculate the centers ofgravity of the initial point set X and the initial target point set P,i.e., μ_(x) and μ_(p) respectively:

$\begin{matrix}{\mu_{x} = {\frac{1}{N_{x}}{\sum\limits_{i = 1}^{N_{x}}x_{i}}}} & (1)\end{matrix}$ $\begin{matrix}{\mu_{p} = {\frac{1}{N_{p}}{\sum\limits_{j = 1}^{N_{p}}{p_{j}.}}}} & (2)\end{matrix}$

In the above formulas, N_(x) is the number of points in the initialpoint set X, x_(i) is the coordinate of the i-th point in the initialpoint set X, i is a positive integer greater than or equal to 1 and lessthan or equal to N_(x); N_(p) is the number of points in the initialtarget point set P, p_(j) the coordinate of the j-th point in theinitial target point set P, and j is a positive integer greater than orequal to 1 and less than or equal to N_(p).

Then, the corresponding centers of gravity are subtracted respectivelyfrom the points of the initial point set X and the initial target pointset P, to obtain the first original point set X′ and the target pointset P′:X′={x _(i)−μ_(x) }={x _(i)′}  (3)P′={p _(j)−μ_(p) }={p _(j)′}  (4).

In the above formulas, x_(i)′ is the coordinate of the i-th point in theoriginal point set X′, and p_(j)′ is the coordinate of the j-th point inthe target point set P′.

In step S204, feature points are extracted from the first original pointset to obtain an original key point set. Since the acquired data of theobject surface is refreshed quickly, if the method of searching for theclosest point is used to register the first original point set and thetarget point set directly, the accuracy will be low due to the slowspeed. Therefore, before searching for the closest point of the firstoriginal point set and the target point set, rough registration may beperformed first based on the key point extraction algorithm to improvethe overall registration speed and accuracy.

In step S206, feature points are extracted from the target point set toobtain a target key point set.

In some embodiments, feature points of the point cloud are some pointsof interest, such as turning points, corner points, and other points orpoint sets with obvious features of a target. To extract key points fromthe point cloud, a Scale-Invariant Feature Transform (SIFT) algorithm,an Intrinsic Shape Signatures (ISS) algorithm, etc. may be used asrequired, which is not limited in the present disclosure.

In step S208, a first registration operation is performed on theoriginal key point set and the target key point set to obtain a firstmodel transformation parameter.

In some embodiments, for each original key point in the original keypoint set, each target key point in the target key point set istraversed to obtain the closest point pair in the original key point setand the target key point set. The closest point pair includes oneoriginal key point in the original key point set and one target keypoint in the target key point set. The distance between the one originalkey point and the one target key point is less than or equal to a firstpreset threshold, which may be expressed as the following formula:

$\begin{matrix}{\sqrt{{\sum\limits_{m = 1}^{N_{q}}{\sum\limits_{l = 1}^{N_{y}}{{y_{l} - q_{m}}}^{2}}} \leq C}.} & (5)\end{matrix}$

In the above formula, N_(y) is the number of original key points in theoriginal key point set, y_(l) is the coordinate of the 1-th point in theoriginal key point set, 1 is a positive integer greater than or equal to1 and less than or equal to N_(y); number, N_(q) is the number of targetkey points in the target key point set, q_(m) is the coordinate of them-th point in the target key point set, m is a positive integer greaterthan or equal to 1 and less than or equal to N_(q). The first modeltransformation parameter is then obtained from the closest point pair.The first model transformation parameter may include a rotationparameter and a displacement parameter.

In some embodiments, the following method may be used to obtain theclosest point pair in the original key point set and the target keypoint se. Firstly, for each original key point in the original key pointset, the corresponding closest point in the target key point set isobtained by traversing, and multiple closest point pairs in the originalkey point set and the target key point set are obtained, therebyobtaining multiple candidate model transformation parameterscorresponding to the multiple closest point pairs. For each closestpoint pair in the multiple closest point pairs, the transformation errorwith respect to the one target key point in the closest point pair isobtained of each of other original key points in the original key pointset, except for the one original key point in the closest point pair,after transformation based on the candidate model transformationparameter corresponding to the closest point pair. The transformationerror of the original key point may be obtained by subtracting theoriginal key point after transformation based on the modeltransformation parameter from the corresponding target key point, andthen squaring. After that, error of each of the other original keypoints is added to obtain the transformation error. The number of theother original key points whose transformation error corresponding toeach closest point pair is less than or equal to the second presetthreshold may be acquired. The candidate model transformation parametercorresponding to the closest point pair with the largest number of theother original key points whose transformation error is less than orequal to the second preset threshold may be selected as the first modeltransformation parameter.

In step S210, the first original point set is transformed based on thefirst model transformation parameter to obtain a second original pointset. The transformed points obtained by transforming each of the firstoriginal points in the first original point set based on the first modeltransformation parameter are in one-to-one correspondence with thetarget points in the target point set. After the first modeltransformation parameter is obtained through rough registration, thefirst original point set is rotated and translated based on the firstmodel transformation parameter to obtain a second original point set tobe finely registered.

In step S212, a second registration operation is performed on the secondoriginal point set and the target point set to obtain a second modeltransformation parameter. Accurate registration generally adopts theIterative Closest Point (ICP) method. For each point in the secondoriginal point set, the closest point in the matching target point setis searched for, to obtain the corresponding rotation parameter R andtranslation parameter t, and then the total error E(R,t) is calculatedby the following formula:

$\begin{matrix}{{E\left( {R,t} \right)} = {\frac{1}{N_{k}}{\sum\limits_{k = 1}^{N_{k}}{{{p_{k}^{\prime} - {R \cdot x_{k}^{\prime}} - t}}^{2}.}}}} & (6)\end{matrix}$

In the above formula, x_(k)′∈X′, p_(k)′∈P′, N_(k) are the number ofclosest point pairs in the closest point pair set, and N_(k) is apositive integer greater than or equal to 1, less than or equal to N_(x)and less than or equal to N_(p), and k is a positive integer greaterthan or equal to 1 and less than or equal to N_(k). If the calculatedtotal error is greater than a preset threshold, the second originalpoint set is transformed according to the rotation parameter R and thetranslation parameter t to obtain an updated second original point set,continuing to search for the closest point in the matching target pointset, and repeating the above steps, until the total error is not greaterthan the preset threshold, thus obtaining the second modeltransformation parameter.

In some embodiments, the calculation method of the total error may alsouse the root mean square difference or the like. The above-mentionedcondition for stopping the iteration and obtaining the second modeltransformation parameter may also be that the absolute values of twoconsecutive root mean square differences are less than a certaintolerance, or may be that the number of iterations has reached a presetnumber. A method based on singular value decomposition, or a quaternionmethod, etc. may also be used to make the total error converge. Thepresent disclosure is not limited thereto.

In step S214, a third model transformation parameter is obtainedaccording to the first model transformation parameter and the secondmodel transformation parameter, and the third model transformationparameter is used to enable the mobile device to obtain data of theobject surface for positioning by registering the first original pointset and the target point set.

According to the method for processing positioning data of a mobiledevice provided by an embodiment of the present disclosure, the firstoriginal point set and the target point set are obtained by measuringthe object surface with the mobile device, and feature points arerespectively extracted from the first original point set and the targetpoint set to obtain the original key point set and the target key pointset. Then, the first registration operation is performed on the originalkey point set and the target key point set to obtain the first modeltransformation parameter, and the first original point set istransformed based on the first model transformation parameter to obtainthe second original point set. After that, a second registrationoperation is performed on the second original point set and the targetpoint set to obtain the second model transformation parameter. A thirdmodel transformation parameter is obtained based on the first modeltransformation parameter and the second model transformation parameterfor registering the first original point set and the target point set,which enables the mobile device to obtain the data of the object surfacefor positioning, so that the processing accuracy of the positioning dataof the mobile device can be improved to a certain extent.

FIG. 3 is a flow chart of a method for extracting feature points ofpositioning data of a mobile device according to an exemplaryembodiment. The method shown in FIG. 3 may be applied to, for example,the server side of the system for processing positioning data of amobile device, and may be alternatively applied to the terminal deviceof the system for processing positioning data of a mobile device. Instep S204 and step S206, this method may be used to extract featurepoints in the point set.

Referring to FIG. 3 , the method 30 provided by an embodiment of thepresent disclosure may include the following steps S302, S304, S306, andS308.

In step S302, the space formed by the first original point set isdivided into a plurality of voxel grids with a first preset side length.For example, for the first original point set consisting ofthree-dimensional data, it is divided using unit cubes of the same size.

In step S304, the center of gravity of the original point included ineach voxel grid of the plurality of voxel grids with the first presetside length is calculated, and the set of the centers of gravity of eachvoxel grid is the original key point set. The calculation method of thecenter of gravity refers to formula (1) and formula (2).

In step S306, the space formed by the first target point set is dividedinto a plurality of voxel grids with a second preset side length. Forexample, for the first original point set consisting ofthree-dimensional data, it is divided using unit cubes of the same size.

In step S308, the center of gravity of the target point included in eachvoxel grid of the plurality of voxel grids with the first preset sidelength is calculated, and the set of the centers of gravity of eachvoxel grid is the target key point set. The calculation method of thecenter of gravity refers to formula (1) and formula (2).

According to the method for extracting feature points of positioningdata of a mobile device provided by embodiments of the presentdisclosure, after dividing the first original point set and the firsttarget point set into voxel grids, the centers of gravity of the pointsin the voxel grid are selected as the original key point and the targetkey point, so that the first registration operation is performed on theoriginal key point set and the target key point set to obtain the firstmodel transformation parameter. Thus, the rough registration operationof the positioning data of the mobile device can be realized, and thespeed and precision of the fine registration can be improved to acertain extent.

FIG. 4 is a flow chart of a method for processing positioning data of amobile device according to an exemplary embodiment. The method shown inFIG. 4 may be applied to, for example, the server side of the system forprocessing positioning data of a mobile device, and may be alternativelyapplied to the terminal device of the system for processing positioningdata of a mobile device.

Referring to FIG. 4 , the method 40 provided by an embodiment of thepresent disclosure may include the following steps S402, S404, S406,S408, S410, S412, and S414.

In step S402, the first original point set and the target point set areobtained by the indoor robot measuring the object surface with thelidar.

In step S404, feature points are extracted from the first original pointset based on the voxel grid scale-invariant feature transformationmethod to obtain an original key point set. Firstly, the first originalpoint set is divided according to the voxel grid division method tocreate a voxel grid model. Then, each voxel grid model is convolved witha three-dimensional Gaussian filter to obtain the spatial scale of thevoxel grid model. The spatial scale model of the each voxel grid modelis subtracted by the original model itself to establish a Gaussiandifference model. This can ensure that the voxel grid has scaleinvariance related to it. A weighted histogram is built for the 3Dneighborhood around the given extreme points and finally the originalkey point set is generated.

In step S406, similar to step S404, feature points are extracted fromthe target point set based on the voxel grid scale-invariant featuretransformation method to obtain a target key point set.

In step S408, a first registration operation is performed on theoriginal key point set and the target key point set to obtain a firstmodel transformation parameter.

In step S410, the first original point set is transformed based on thefirst model transformation parameter to obtain a second original pointset.

For some implementations of steps S408 to S410, reference may be made tosteps S208 to S210, which will not be repeated here.

In step S412, a second registration operation is performed on the secondoriginal point set and the target point set to obtain a second modeltransformation parameter.

In some embodiments, in step S4121, the variance in each dimension ofeach target point in the target point set is calculated.

In step S4122, the dimension with the largest variance is selected asthe registration dimension.

In step S4123, a high-dimensional index binary tree of the target pointset in the registration dimension is constructed. The high-dimensionalindex binary tree (K-Dimensional Tree, KD Tree) is a data structure thatdivides high-dimensional data space, and is mainly used for NearestNeighbor and Approximate Nearest Neighbor of key data inmulti-dimensional space. KD Tree is a variant of Binary Search Tree(BST). The properties of a binary search tree are as follows. If itsleft subtree is not empty, the value of all nodes on the left subtree isless than the value of its root node. If its right subtree is not empty,then the value of all nodes on the right subtree is greater than thevalue of its root node. Its left and right subtrees are also binary sorttrees. The construction method of Kd-Tree is as follows. The dimensionwith the largest variance is selected in the K-dimensional data set, andthen the median (arithmetic mean) on this dimension is selected as thedividing point to divide the K-dimensional data set so as to obtain twosubsets. In the meanwhile, a tree node is created for storage. Then, theprocess of selecting the median and dividing in the previous step isrepeated for the two subsets, until all the subsets can no longer bedivided.

In step S4124, the closest point in the target point set of each secondoriginal point in the second original point set is searched for by usingthe high-dimensional index binary tree. Firstly, it is started from theroot node of the high-dimensional index binary tree (i.e., the firstdivision point) for each second original point, and the high-dimensionalindex is queried downward according to the comparison result betweeneach second original point and each node of the high-dimensional indexbinary tree, until a leaf node is reached (that is, the node that cannotbe further divided down, and the upper-level node has only this node inthe branch subset). Then, a backtracking operation is performed todetermine whether the distance between the node of the unqueried branchfor each second original point on the high-dimensional index binary treeand the second original point is not less than the leaf node. The nodeson the unqueried branch are sorted according to the closeness of thevalue of the registration dimension with respect to each second originalpoint, that is, according to the absolute value of the differencebetween the value of the second original point and the unqueried node inthe registration dimension. The smaller the absolute value, the moreahead the sorting. The, the priority node sequence is obtained. Eachnode on the unqueried branch of each second original point on thehigh-dimensional index binary tree is queried in turn according to thepriority node sequence, and it is determined whether the distancebetween each node on the unqueried branch and each of the secondoriginal points is not less than the leaf node. If the distance betweenthe node on the unqueried branch and each of the second original pointsis smaller than the leaf node, it is determined that the node on theunqueried branch for each of the second original points is the closestpoint.

In step S4125, the distance between each second original point and thecorresponding closest point is calculated respectively.

In step S4126, the model transformation parameter, with respect to arespective closest point, of the second original point in the secondoriginal point set with the smallest distance to the respective closestpoint is selected as the second model transformation parameter. Thesecond model transformation parameter can be obtained by an iterativeclosest point method, see some embodiments about step S212, and detailsare not repeated here.

In step S414, a third model transformation parameter is obtained basedon the first model transformation parameter and the second modeltransformation parameter, and the third model transformation parameteris used to enable the mobile device to obtain data of the object surfacefor positioning by registering the first original point set and thetarget point set.

According to the method for processing positioning data of a mobiledevice provided by an embodiment of the present disclosure, the firstoriginal point set and the target point set are obtained by measuringthe object surface with the lidar. A method based on the voxelscale-invariant feature transformation is used to extract feature pointsfrom the first original point set and the target point set,respectively, to obtain the original key point set and the target keypoint set. The first registration operation is performed on the originalkey point set and the target key point set to obtain the first modeltransformation parameter. The first original point set is transformed bythe first model transformation parameter to obtain the second originalpoint set. Then, the K-D Tree method is used to perform the secondregistration operation on the second original point set and the targetpoint set, to obtain the second model transformation parameter. A thirdmodel transformation parameter is obtained based on the first modeltransformation parameter and the second model transformation parameterfor registering the first original point set and the target point set,which enables the mobile device to obtain the data of the object surfacefor positioning, so that accuracy and robustness for processing thepositioning data of the mobile device can be improved to a certainextent.

FIG. 5 is a flow chart of an apparatus for processing positioning dataof a mobile device according to an exemplary embodiment. The apparatusshown in FIG. 5 may be applied to, for example, the server side of thesystem for processing positioning data of a mobile device, and may bealternatively applied to the terminal device of the system forprocessing positioning data of a mobile device.

Referring to FIG. 5 , the apparatus 50 provided by an embodiment of thepresent disclosure may include: a data acquisition module 502, a featureextraction module 504, a first registration module 506, a secondregistration module 508, and a model acquisition module 510.

The data acquisition module 502 may be used to acquire the firstoriginal point set and the target point set by measuring the objectsurface with the mobile device.

The feature extraction module 504 may be used to extract feature pointsfrom the first original point set to obtain the original key point set.

The feature extraction module 504 may also be used to extract featurepoints from the target point set to obtain the target key point set.

The first registration module 506 may be configured to perform a firstregistration operation on the original key point set and the target keypoint set to obtain a first model transformation parameter.

The first registration module 506 may also be configured to transformthe first original point set based on the first model transformationparameter to obtain the second original point set.

The second registration module 508 may be configured to perform a secondregistration operation on the second original point set and the targetpoint set to obtain a second model transformation parameter.

The model obtaining module 510 may be configured to obtain a third modeltransformation parameter according to the first model transformationparameter and the second model transformation parameter, and the thirdmodel transformation parameter is used to enable the mobile device toobtain data of the object surface for positioning by registering thefirst original point set and the target point set.

The first registration module 506 may also be used to obtain the closestpoint pair in the original key point set and the target key point set,where the closest point pair includes one original key point in theoriginal key point set and one target key point in the target key pointset, and the distance between the one original key point and the onetarget key point is less than or equal to the first preset threshold.The first model transformation parameter is obtained according to theclosest point pair.

The first registration module 506 may also be used to obtain multipleclosest point pairs in the original key point set and the target keypoint set; and to obtain multiple candidate model transformationparameters corresponding to multiple closest point pairs. For eachclosest point pair, a transformation error, with respect to the onetarget key point in the closest point pair, is calculated of each ofother original key points in the original key point set, except for theone original key point in the closest point pair, after transformationbased on a candidate model transformation parameter corresponding to theclosest point pair. Further, the number of the other original key pointswhose transformation error corresponding to each closest point pair isless than or equal to a second preset threshold is acquired. Then, acandidate model transformation parameter corresponding to the closestpoint pair with the largest number of the other original key pointswhose transformation error is less than or equal to the second presetthreshold is selected as the first model transformation parameter.

The feature extraction module 504 may also be used to divide the spaceformed by the first original point set into a plurality of voxel gridswith a first preset side length; and to calculate the center of gravityof the original point contained in each voxel grid of the plurality ofvoxel grids with the first preset side length, where the set of thecenters of gravity of each voxel grid is the original key point set.Further, the space formed by the first target point set is divided intoa plurality of voxel grids with a second preset side length. The centerof gravity of the target point included in each voxel grid of the voxelgrids with the second preset side length is calculated, and the set ofthe centers of gravity of each voxel grid is the target key point set.

The second registration module 508 may also be used to calculate thevariance in each dimension of each target point in the target point set;select the dimension with the largest variance as the registrationdimension; construct a high-dimensional index binary tree of the targetpoint set in the registration dimension; search for the closest point inthe target point set of each second original point in the secondoriginal point set by using the high-dimensional index binary tree;calculates the distance between each second original point and thecorresponding closest point respectively; and select, as the secondmodel transformation parameter, the model transformation parameter, withrespect to the respective closest point, of the second original point inthe second original point set with the smallest distance to therespective closest point.

The second registration module 508 may also be configured to start fromthe root node of the high-dimensional index binary tree for each secondoriginal point, and query the high-dimensional index binary treedownward according to the comparison result between each second originalpoint and each node of the high-dimensional index binary tree, until theleaf node is reached; determine whether the distance between the node ofthe unqueried branch for each second original point on thehigh-dimensional index binary tree and the second original point is notless than the leaf node; and if the distance between the node on theunqueried branch and each second original point is smaller than the leafnode, then it is determined that the node of the unqueried for eachsecond original point is the closest point.

The second registration module 508 may also be configured to sort eachnode of the unqueried branch according to the closeness of the value ofthe registration dimension with respect to each second original point toobtain a priority node sequence; and according to the priority nodesequence, sequentially query each node of the unqueried branch for eachsecond original point on the high-dimensional index binary tree, todetermine whether the distance between each node of the unqueried branchand the second original point is not less than the leaf node.

FIG. 6 is a block diagram of an electronic device that can be used inthe system for processing positioning data of a mobile device accordingto an exemplary embodiment. It should be noted that the device shown inFIG. 6 is only an example of a computer system, and should not imposeany limitations on the functions and scope in use of embodiments of thepresent disclosure.

As shown in FIG. 6 , the apparatus 600 includes a central processingunit (CPU) 601, which can be used to perform various appropriate actionsand processes according to a program stored in a read only memory (ROM)602 or a program loaded from a storage part 608 into a random accessmemory (RAM) 603. In the RAM 603, various programs and data necessaryfor operations of the apparatus 600 are also stored. The CPU 601, theROM 602, and the RAM 603 are connected to each other through a bus 604.An input/output (I/O) interface 605 is also connected to bus 604.

The following components are connected to the I/O interface 605: aninput part 606 including a keyboard, a mouse, etc.; an output part 607including a cathode ray tube (CRT), a liquid crystal display (LCD), anda speaker, etc.; a storage part 608 including a hard disk, etc.; and acommunication part 609 including a network interface card such as a LANcard, a modem, and the like. The communication part 609 performscommunication processing via a network such as the Internet. A drive 610is also connected to the I/O interface 605 as needed. A removable medium611, such as a magnetic disk, an optical disk, a magneto-optical disk, asemiconductor memory, etc., is mounted on the drive 610 as needed, sothat a computer program read therefrom is installed into the storagepart 608 as needed.

In particular, according to embodiments of the present disclosure, theprocesses described above with reference to the flowcharts may beimplemented as computer software programs. For example, embodiments ofthe present disclosure include a computer program product comprising acomputer program carried on a computer-readable medium. The computerprogram contains program code for performing the method illustrated inthe flowchart. In such an embodiment, the computer program may bedownloaded and installed from the network via the communication part 609and/or installed from the removable medium 611. When the computerprogram is executed by the central processing unit (CPU) 601, theabove-described functions defined in the system of the presentdisclosure are executed.

It should be noted that the computer-readable medium shown in thepresent disclosure may be a computer-readable signal medium or acomputer-readable storage medium, or any combination of the above two.The computer-readable storage medium can be, for example, but notlimited to, an electrical, magnetic, optical, electromagnetic, infrared,or semiconductor system, apparatus or device, or a combination of any ofthe above. More specific examples of computer readable storage media mayinclude, but are not limited to, electrical connections with one or morewires, portable computer disks, hard disks, random access memory (RAM),read only memory (ROM), erasable programmable read only memory (EPROM orflash memory), optical fiber, portable compact disk read only memory(CD-ROM), optical storage devices, magnetic storage devices, or anysuitable combination of the above. In the present disclosure, acomputer-readable storage medium may be any tangible medium thatcontains or stores a program that can be used by or in conjunction withan instruction execution system, apparatus, or device. In the presentdisclosure, a computer-readable signal medium may include a data signalpropagated in baseband or as part of a carrier wave, carryingcomputer-readable program code therein. Such propagated data signals maytake a variety of forms, including but not limited to electromagneticsignals, optical signals, or any suitable combination of the foregoing.A computer-readable signal medium can also be any computer-readablemedium other than a computer-readable storage medium that can transmit,propagate, or transport the program for use by or in connection with theinstruction execution system, apparatus, or device. Program codeembodied on a computer readable medium may be transmitted using anysuitable medium including, but not limited to, wireless, wireline,optical fiber cable, RF, etc., or any suitable combination of theforegoing.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code that contains one or more executable instructions forimplementing the specified logical functions. It should also be notedthat, in some alternative implementations, the functions noted in theblocks may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It is also noted thateach block of the block diagrams or flowchart illustrations, andcombinations of blocks in the block diagrams or flowchart illustrations,can be implemented in dedicated hardware-based systems that perform thespecified functions or operations, or can be implemented using acombination of dedicated hardware and computer instructions.

The modules involved in embodiments of the present disclosure may beimplemented in software or hardware. The described modules can also beprovided in a processor. For example, it may be described as follows.The processor includes a data acquisition module, a feature extractionmodule, a first registration module, a second registration module and amodel acquisition module. Names of these modules do not constitute alimitation of each module itself under certain circumstances. Forexample, the data acquisition module can also be described as “a modulefor acquiring point set data from the connected server”.

As another aspect, the present disclosure also provides acomputer-readable medium. The computer-readable medium may be includedin the device described in the above-mentioned embodiments, or it mayexist alone without being assembled into the device. The above-mentionedcomputer-readable medium carries one or more programs. When theabove-mentioned one or more programs are executed by a device, thedevice is enabled to: acquire a first original point set and a targetpoint set by measuring the object surface with a mobile device; extractfeature points from the first original point set to obtain the originalkey point set; extract feature points from the target point set toobtain the target key point set; perform the first registrationoperation on the original key point set and the target key point set toobtain the first model transformation parameter; transform the firstoriginal point set by the first model transformation parameter to obtainthe second original point set; perform the second registration operationon the second original point set and the target point set to obtain thesecond model transformation parameter; and obtain a third modeltransformation parameter according to the first model transformationparameter and the second model transformation parameter, where the thirdmodel transformation parameter is used to enable the mobile device toobtain the data of the object surface for positioning by registering thefirst original point set and the target point set.

Exemplary embodiments of the present disclosure have been specificallyshown and described above. It should be understood that the presentdisclosure is not limited to the details of construction, arrangements,or implementations described herein. On the contrary, the presentdisclosure is intended to cover various modifications and equivalentarrangements included within the spirit and scope of the appendedclaims.

The invention claimed is:
 1. A method for processing positioning data ofa mobile device, comprising: acquiring a first original point set and atarget point set by measuring an object surface with the mobile device;extracting feature points from the first original point set to obtain anoriginal key point set; extracting feature points from the target pointset to obtain a target key point set; performing a first registrationoperation on the original key point set and the target key point set toobtain a first model transformation parameter; transforming the firstoriginal point set by the first model transformation parameter to obtaina second original point set; performing a second registration operationon the second original point set and the target point set to obtain asecond model transformation parameter; and acquiring a third modeltransformation parameter based on the first model transformationparameter and the second model transformation parameter, wherein thethird model transformation parameter is configured to enable the mobiledevice to obtain data of the object surface for positioning byregistration on the first original point set and the target point set.2. The method according to claim 1, wherein, said performing the firstregistration operation on the original key point set and the target keypoint set to obtain the first model transformation parameter, comprises:acquiring a closest point pair in the original key point set and thetarget key point set, wherein the closest point pair comprises oneoriginal key point in the original key point set and one target keypoint in the target key point set, and a distance between the oneoriginal key point and the one target key point is less than or equal toa first preset threshold; and acquiring the first model transformationparameter based on the closest point pair.
 3. The method according toclaim 2, wherein said acquiring the closest point pair in the originalkey point set and the target key point set, comprises: acquiring aplurality of closest point pairs in the original key point set and thetarget key point set; and said acquiring the first model transformationparameter based on the closest point pair, comprises: acquiring aplurality of candidate model transformation parameters corresponding tothe plurality of closest point pairs; for each closest point pair in theplurality of closest point pairs, calculating a transformation error,with respect to the one target key point in the closest point pair, ofeach of other original key points in the original key point set, exceptfor the one original key point in the closest point pair, aftertransformation based on a candidate model transformation parametercorresponding to the closest point pair; acquiring a number of the otheroriginal key points whose transformation error corresponding to eachclosest point pair is less than or equal to a second preset threshold;and selecting, as the first model transformation parameter, a candidatemodel transformation parameter corresponding to the closest point pairwith the largest number of the other original key points whosetransformation error is less than or equal to the second presetthreshold.
 4. The method according to claim 1, wherein, said extractingthe feature points from the first original point set to obtain theoriginal key point set, and said extracting the feature points from thetarget point set to obtain the target key point set, comprises: dividinga space formed by the first original point set into a plurality of voxelgrids with a first preset side length; calculating a center of gravityof an original point included in each voxel grid of the plurality ofvoxel grids with the first preset side length, wherein a set of thecenters of gravity of each voxel grid is the original key point set;dividing a space formed by the first target point set into a pluralityof voxel grids with a second preset side length; and calculating acenter of gravity of a target point included in each voxel grid of theplurality of voxel grids with the second preset side length, wherein aset of the centers of gravity of each voxel grid is the target key pointset.
 5. The method according to claim 1, wherein, said performing thesecond registration operation on the second original point set and thetarget point set to obtain the second model transformation parameter,comprises: calculating a variance in each dimension of each target pointin the target point set; selecting a dimension with the largest varianceas a registration dimension; constructing a high-dimensional indexbinary tree in the registration dimension of the target point set;searching for a closest point in the target point set of each secondoriginal point in the second original point set by using thehigh-dimensional index binary tree; calculating a distance between eachsecond original point and a respective closest point; and selecting, asthe second model transformation parameter, a model transformationparameter, with respect to a respective closest point, of a secondoriginal point in the second original point set with the smallestdistance to the respective closest point.
 6. The method according toclaim 5, wherein, said searching for the closest point in the targetpoint set of each second original point in the second original point setby using the high-dimensional index binary tree, comprises: querying thehigh-dimensional index binary tree downward according to a comparisonresult between each second original point and each node of thehigh-dimensional index binary tree by starting from a root node of thehigh-dimensional index binary tree for each second original point, untila leaf node is reached; determining whether a distance between a node ona unqueried branch for each second original point on thehigh-dimensional index binary tree and the second original point is notless than the leaf node; and if the distance between the node on theunqueried branch and each second original point is smaller than the leafnode, it is determined that the node on the unqueried branch for thesecond original point is the closest point.
 7. The method according toclaim 6, wherein, said determining the distance between the node on theunqueried branch for each second original point on the high-dimensionalindex binary tree and the second original point is not less than theleaf node, comprises: sorting each node on the unqueried branchaccording to closeness of a value of the registration dimension withrespect to each second original point to obtain a priority nodesequence; and based on the priority node sequence, sequentially queryingeach node on the unqueried branch for each second original point on thehigh-dimensional index binary tree, to determine whether a distancebetween each node on the unqueried branch and the second original pointis not less than the leaf node.
 8. An apparatus, comprising: a memory, aprocessor, and executable instructions stored in the memory and runningin the processor, wherein the processor is configured to execute theexecutable instructions to implement a method for processing positioningdata of a mobile device, comprising: acquiring a first original pointset and a target point set by measuring an object surface with themobile device; extracting feature points from the first original pointset to obtain an original key point set; extracting feature points fromthe target point set to obtain a target key point set; performing afirst registration operation on the original key point set and thetarget key point set to obtain a first model transformation parameter;transforming the first original point set by the first modeltransformation parameter to obtain a second original point set;performing a second registration operation on the second original pointset and the target point set to obtain a second model transformationparameter; and acquiring a third model transformation parameter based onthe first model transformation parameter and the second modeltransformation parameter, wherein the third model transformationparameter is configured to enable the mobile device to obtain data ofthe object surface for positioning by registration on the first originalpoint set and the target point set.
 9. The apparatus according to claim8, wherein, said performing the first registration operation on theoriginal key point set and the target key point set to obtain the firstmodel transformation parameter, comprises: acquiring a closest pointpair in the original key point set and the target key point set, whereinthe closest point pair comprises one original key point in the originalkey point set and one target key point in the target key point set, anda distance between the one original key point and the one target keypoint is less than or equal to a first preset threshold; and acquiringthe first model transformation parameter based on the closest pointpair.
 10. The apparatus according to claim 9, wherein said acquiring theclosest point pair in the original key point set and the target keypoint set, comprises: acquiring a plurality of closest point pairs inthe original key point set and the target key point set; and saidacquiring the first model transformation parameter based on the closestpoint pair, comprises: acquiring a plurality of candidate modeltransformation parameters corresponding to the plurality of closestpoint pairs; for each closest point pair in the plurality of closestpoint pairs, calculating a transformation error, with respect to the onetarget key point in the closest point pair, of each of other originalkey points in the original key point set, except for the one originalkey point in the closest point pair, after transformation based on acandidate model transformation parameter corresponding to the closestpoint pair; acquiring a number of the other original key points whosetransformation error corresponding to each closest point pair is lessthan or equal to a second preset threshold; and selecting, as the firstmodel transformation parameter, a candidate model transformationparameter corresponding to the closest point pair with the largestnumber of the other original key points whose transformation error isless than or equal to the second preset threshold.
 11. The apparatusaccording to claim 8, wherein, said extracting the feature points fromthe first original point set to obtain the original key point set, andsaid extracting the feature points from the target point set to obtainthe target key point set, comprises: dividing a space formed by thefirst original point set into a plurality of voxel grids with a firstpreset side length; calculating a center of gravity of an original pointincluded in each voxel grid of the plurality of voxel grids with thefirst preset side length, wherein a set of the centers of gravity ofeach voxel grid is the original key point set; dividing a space formedby the first target point set into a plurality of voxel grids with asecond preset side length; and calculating a center of gravity of atarget point included in each voxel grid of the plurality of voxel gridswith the second preset side length, wherein a set of the centers ofgravity of each voxel grid is the target key point set.
 12. Theapparatus according to claim 8, wherein, said performing the secondregistration operation on the second original point set and the targetpoint set to obtain the second model transformation parameter,comprises: calculating a variance in each dimension of each target pointin the target point set; selecting a dimension with the largest varianceas a registration dimension; constructing a high-dimensional indexbinary tree in the registration dimension of the target point set;searching for a closest point in the target point set of each secondoriginal point in the second original point set by using thehigh-dimensional index binary tree; calculating a distance between eachsecond original point and a respective closest point; and selecting, asthe second model transformation parameter, a model transformationparameter, with respect to a respective closest point, of a secondoriginal point in the second original point set with the smallestdistance to the respective closest point.
 13. The apparatus according toclaim 12, wherein, said searching for the closest point in the targetpoint set of each second original point in the second original point setby using the high-dimensional index binary tree, comprises: querying thehigh-dimensional index binary tree downward according to a comparisonresult between each second original point and each node of thehigh-dimensional index binary tree by starting from a root node of thehigh-dimensional index binary tree for each second original point, untila leaf node is reached; determining whether a distance between a node ona unqueried branch for each second original point on thehigh-dimensional index binary tree and the second original point is notless than the leaf node; and if the distance between the node on theunqueried branch and each second original point is smaller than the leafnode, it is determined that the node on the unqueried branch for thesecond original point is the closest point.
 14. The apparatus accordingto claim 13, wherein, said determining the distance between the node onthe unqueried branch for each second original point on thehigh-dimensional index binary tree and the second original point is notless than the leaf node, comprises: sorting each node on the unqueriedbranch according to closeness of a value of the registration dimensionwith respect to each second original point to obtain a priority nodesequence; and based on the priority node sequence, sequentially queryingeach node on the unqueried branch for each second original point on thehigh-dimensional index binary tree, to determine whether a distancebetween each node on the unqueried branch and the second original pointis not less than the leaf node.
 15. A computer-readable storage medium,having computer-executable instructions stored thereon, wherein thecomputer-executable instructions are configured, when executed by aprocessor, to implement a method for processing positioning data of amobile device, comprising: acquiring a first original point set and atarget point set by measuring an object surface with the mobile device;extracting feature points from the first original point set to obtain anoriginal key point set; extracting feature points from the target pointset to obtain a target key point set; performing a first registrationoperation on the original key point set and the target key point set toobtain a first model transformation parameter; transforming the firstoriginal point set by the first model transformation parameter to obtaina second original point set; performing a second registration operationon the second original point set and the target point set to obtain asecond model transformation parameter; and acquiring a third modeltransformation parameter based on the first model transformationparameter and the second model transformation parameter, wherein thethird model transformation parameter is configured to enable the mobiledevice to obtain data of the object surface for positioning byregistration on the first original point set and the target point set.